As businesses increasingly rely on technology to manage data related to operations such as invoice and purchase order data, suitable systems for properly managing and validating data have become crucial to success. Particularly for large businesses, the amount of data utilized daily by businesses can be overwhelming. Accordingly, manual review and validation of such data is impractical, at best. However, disparities between recordkeeping documents can cause significant problems for businesses such as, for example, failure to properly report earnings to tax authorities.
Some solutions exist for automatically recognizing information in scanned documents (e.g., invoices and receipts) or other unstructured electronic documents (e.g., unstructured text files). Such solutions often face challenges in accurately identifying and recognizing characters and other features of electronic documents. Moreover, degradation in content of the input unstructured electronic documents typically result in higher error rates. As a result, existing image recognition techniques are not completely accurate under ideal circumstances (i.e., very clear images), and their accuracy often decreases dramatically when input images are less clear. Moreover, missing or otherwise incomplete data can result in errors during subsequent use of the data. Many existing solutions cannot identify missing data unless, e.g., a field in a structured dataset is left incomplete.
In addition, existing image recognition solutions may be unable to accurately identify some or all special characters (e.g., “!,” “@,” “#,” “$,” “©,” “%,” “&,” etc.). As an example, some existing image recognition solutions may inaccurately identify a dash included in a scanned receipt as the number “1.” As another example, some existing image recognition solutions cannot identify special characters such as the dollar sign, the yen symbol, etc.
Further, such solutions may face challenges in preparing recognized information for subsequent use. Specifically, many such solutions either produce output in an unstructured format, or can only produce structured output if the input electronic documents are specifically formatted for recognition by an image recognition system. The resulting unstructured output typically cannot be processed efficiently. In particular, such unstructured output may contain duplicates, and may include data that requires subsequent processing prior to use.
Enterprises all over the world usually spend large amounts of money on business services and goods purchased by employees in the course of employment. In most cases these transactions may be refundable as the enterprise can reclaim the value added tax (VAT) as well as deducting qualified expenses from the corporate income tax (CIT). Such expenses should be reported to appropriate tax authorities in order to reclaim at least a partial tax refund for the expenses made.
In many cases, depending on the regulation in the particular jurisdiction, enterprises are required to provide expense evidences, such as for example, receipts, invoices, and the like, associated with the expenses made, as well as a statement indicating the type and amount of expense. The expense report together with the respective evidences is provided to the tax authorities in order to reclaim the VAT and/or to deduct the CIT associated with the transaction.
In case an evidence does not include one or more of the necessary elements, such as for example, supplier name, supplier address, supplier ID, date, amount, etc., the enterprise may not be able to use this evidence for VAT reclaiming and/or CIT deducting. In order to deal with this issue and get the full VAT reclaim and/or CIT deduction, enterprises need to devote significant time and resources. One popular, however expensive, solution is to hire the services of an accounting firm to handle this important financial matter.
Although some existing solutions provide techniques by which enterprises collect and analyze data associated with expenses made by their employees, the usage made with this data is still limited. In particular, such solutions may face challenges in efficiently and accurately identifying documents lacking required data. Further, such solutions do not automatically reissue documents identified as lacking the required data.
It would be therefore advantageous to provide a solution that overcomes the limitations of the prior art by providing an efficient method for reissuing expense evidences.